Accuracy continuous glucose monitoring method, system, and device

ABSTRACT

A method, system, and device for improving the accuracy of a continuous glucose monitoring sensor by estimating a CGM signal at a time t+PH using a value of CGM at time t, using a real-time short-time glucose prediction horizon to estimate the real time denoised CGM value with a noise estimation algorithm.

CROSS-REFERENCE TO RELATED APPLICATION AND CLAIM OF PRIORITY

This application claims priority under 35 U.S.C. 119(e) from U.S.Provisional Application Ser. No. 62/037,133 filed Aug. 14, 2014, whichis incorporated herein by reference in its entirety.

FIELD

A method, system, and device for improving the accuracy of continuousglucose monitoring through short-time prediction. For example, theaccuracy of a continuous monitoring sensor is improved.

BACKGROUND

The advent of continuous glucose monitoring (CGM) provided animprovement in the control and understanding of glucose levels indiabetic patients [1]. The quasi-continuous data stream allowscollecting information about glucose variability, detection, andquantification of the duration of hypo- and hyper-glycemic events [2].Clinically, the analysis of CGM data, either in real time orretrospective, is extremely useful in the management of diabetes [3,4].

In terms of technology advancement, CGM sensors coupled with an insulinpump carry a promise for the design and development of artificialpancreas and automated closed-loop control [5-7]. Advisory devices,which suggest actions in real time, are also under investigation, e.g.the one developed for the DIAdvisor project [8]. The present inventorsrecognize that a crucial aspect for the success of these devices is theaccuracy of the CGM sensors. Because CGM sensors measure interstitialglucose (IG) rather than blood glucose (BG) directly, the accuracy ofCGM readings is suboptimal [9,10].

To illustrate the problem, FIG. 1 shows an example of CGM time series(line), collected every minute using the FreeStyle Navigator® (AbbottDiabetes Care, Alameda, Calif.), and compared to high frequentlymeasured BG references, obtained every 15 minutes with YSI BG analyzer(YSI, Inc., Yellow Springs, Ohio). It is evident that the CGM tracediverges from the BG trace, both in terms of amplitude and delay. Thedifference, which is mostly evident during the rising and fallingfronts, is typically attributed to the kinetics between BG and IGconcentrations, which acts as a low-pass filter between the two sites[11,12].

SUMMARY

Various objects and advantages of the preferred embodiments of thepresent invention will be appreciated based on this disclosure.According to the preferred embodiments, the present invention improvesthe accuracy of glucose monitoring sensors through short-timeprediction.

As an exemplary embodiment of the invention, a method for improving theaccuracy of a continuous glucose monitoring sensor (CGS) comprising orconsisting of improving accuracy of CGM readings by reducing randomnoise and calibration errors using real-time short-time glucoseprediction.

As a further exemplary embodiment of the invention, a method forimproving the accuracy of a continuous glucose monitoring sensor (CGS)comprising or consisting of improving accuracy of CGM readings byreducing random noise and calibration errors using real-time short-timeglucose prediction; with a prediction horizon (PH) of less than 20minutes.

As an even further exemplary embodiment of the invention, a method forimproving the accuracy of a continuous glucose monitoring sensor (CGS)comprising or consisting of improving accuracy of CGM readings byreducing random noise and calibration errors using real-time short-timeglucose prediction; and compensating part of a delay introduced bylow-pass nature of BG-to-IG kinetic system.

As yet another exemplary embodiment of the invention, a method forimproving the accuracy of a continuous glucose monitoring sensor (CGS)comprising or consisting of improving accuracy of CGM readings byreducing random noise and calibration errors using real-time short-timeglucose prediction; predicting a horizon PH of less than 20 minutes; andcompensating part of a delay introduced by low-pass nature of BG-to-IGkinetic system.

As yet a further exemplary embodiment of the invention, a method forimproving the accuracy of a continuous glucose monitoring sensor (CGS)comprising or consisting of improving accuracy of CGM readings byreducing random noise and calibration errors using real-time short-timeglucose prediction; and substituting a current CGM value given in outputby the sensors at time t, named CGM(t), with the glucose concentrationpredicted by an algorithm PH minutes ahead in time.

As an even further exemplary embodiment of the invention, a method forimproving the accuracy of a continuous glucose monitoring sensor (CGS)comprising or consisting of improving accuracy of CGM readings byreducing random noise and calibration errors using real-time short-timeglucose prediction; substituting a current CGM value given in output bythe sensors at time t, named CGM(t), with the glucose concentrationpredicted by an algorithm PH minutes ahead in time, wherein thealgorithm PH minutes ahead in time is CGMNEW(t)=COM(t+PHIt).

As another exemplary embodiment of the invention, a method for improvingthe accuracy of a continuous glucose monitoring sensor (CGS) comprisingor consisting of improving accuracy of CGM readings by reducing randomnoise and calibration errors using real-time short-time glucoseprediction; substituting a current CGM value given in output by thesensors at time t, named CGM(t), with the glucose concentrationpredicted by an algorithm PH minutes ahead in time; and developing thealgorithm in a stochastic context and implemented using a Kalman filter.

As a further exemplary embodiment of the invention, a method forimproving the accuracy of a continuous glucose monitoring sensor (CGS)comprising or consisting of improving accuracy of CGM readings byreducing random noise and calibration errors using real-time short-timeglucose prediction; and using CGM data only intended for real-timeapplication.

As another further exemplary embodiment of the invention, a method forimproving the accuracy of a continuous glucose monitoring sensor (CGS)comprising or consisting of improving accuracy of CGM readings byreducing random noise and calibration errors using real-time short-timeglucose prediction; and denoising by using a Kalman filter (KF) coupledwith a Bayesian smoothing criterion for the estimation of its unknownparameters.

As an exemplary embodiment of the invention, a system for improving theaccuracy of a continuous glucose monitoring sensor comprising orconsisting of a digital processor; a continuous glucose monitoring (CGM)sensor in communication with the digital processor, the continuousglucose monitoring (CGM) sensor configured to generate a glucose signal;and a denoising module, configured to receive the glucose signal fromthe continuous glucose monitoring (CGM) sensor, and generate an improvedaccuracy CGM signal by reducing random noise and calibration errorsusing real-time short-time glucose prediction.

As another exemplary embodiment of the invention, a system for improvingthe accuracy of a continuous glucose monitoring sensor comprising orconsisting of a digital processor; a continuous glucose monitoring (CGM)sensor in communication with the digital processor, the continuousglucose monitoring (CGM) sensor configured to generate a glucose signal;and a denoising module, configured to receive the glucose signal fromthe continuous glucose monitoring (CGM) sensor, and generate an improvedaccuracy CGM signal by reducing random noise and calibration errorsusing real-time short-time glucose prediction, wherein the denoisingmodule is configured to predict a horizon PH of less than 20 minutes.

As a further exemplary embodiment of the invention, a system forimproving the accuracy of a continuous glucose monitoring sensorcomprising or consisting of a digital processor; a continuous glucosemonitoring (CGM) sensor in communication with the digital processor, thecontinuous glucose monitoring (CGM) sensor configured to generate aglucose signal; and a denoising module, configured to receive theglucose signal from the continuous glucose monitoring (CGM) sensor, andgenerate an improved accuracy CGM signal by reducing random noise andcalibration errors using real-time short-time glucose prediction,wherein the denoising module is configured to compensate part of a delayintroduced by low-pass nature of BG-to-IG kinetic system.

As an even further exemplary embodiment of the invention, a system forimproving the accuracy of a continuous glucose monitoring sensorcomprising or consisting of a digital processor; a continuous glucosemonitoring (CGM) sensor in communication with the digital processor, thecontinuous glucose monitoring (CGM) sensor configured to generate aglucose signal; and a denoising module, configured to receive theglucose signal from the continuous glucose monitoring (CGM) sensor, andgenerate an improved accuracy CGM signal by reducing random noise andcalibration errors using real-time short-time glucose prediction,wherein the denoising module is configured to substitute a current CGMvalue given in output by the sensors at time t, named CGM(t), with theglucose concentration predicted by an algorithm PH minutes ahead intime.

As yet another exemplary embodiment of the invention, a system forimproving the accuracy of a continuous glucose monitoring sensorcomprising or consisting of a digital processor; a continuous glucosemonitoring (CGM) sensor in communication with the digital processor, thecontinuous glucose monitoring (CGM) sensor configured to generate aglucose signal; and a denoising module, configured to receive theglucose signal from the continuous glucose monitoring (CGM) sensor, andgenerate an improved accuracy CGM signal by reducing random noise andcalibration errors using real-time short-time glucose prediction,wherein the denoising module is configured to substitute a current CGMvalue given in output by the sensors at time t, named CGM(t), with theglucose concentration predicted by an algorithm PH minutes ahead intime, and wherein the algorithm PH minutes ahead in time isCGMNEW(t)=COM(t+PHIt).

As yet a further exemplary embodiment of the invention, a system forimproving the accuracy of a continuous glucose monitoring sensorcomprising or consisting of a digital processor; a continuous glucosemonitoring (CGM) sensor in communication with the digital processor, thecontinuous glucose monitoring (CGM) sensor configured to generate aglucose signal; and a denoising module, configured to receive theglucose signal from the continuous glucose monitoring (CGM) sensor, andgenerate an improved accuracy CGM signal by reducing random noise andcalibration errors using real-time short-time glucose prediction,wherein the denoising module is configured to substitute a current CGMvalue given in output by the sensors at time t, named CGM(t), with theglucose concentration predicted by an algorithm PH minutes ahead intime, wherein the algorithm PH minutes ahead in time isCGMNEW(t)=COM(t+PHIt), and wherein the algorithm is developed in astochastic context and implemented using a Kalman filter.

As yet an even further exemplary embodiment of the invention, a systemfor improving the accuracy of a continuous glucose monitoring sensorcomprising or consisting of a digital processor; a continuous glucosemonitoring (CGM) sensor in communication with the digital processor, thecontinuous glucose monitoring (CGM) sensor configured to generate aglucose signal; and a denoising module, configured to receive theglucose signal from the continuous glucose monitoring (CGM) sensor, andgenerate an improved accuracy CGM signal by reducing random noise andcalibration errors using real-time short-time glucose prediction,wherein the denoising module is configured to substitute a current CGMvalue given in output by the sensors at time t, named CGM(t), with theglucose concentration predicted by an algorithm PH minutes ahead intime, wherein the algorithm PH minutes ahead in time isCGMNEW(t)=COM(t+PHIt), wherein the algorithm is developed in astochastic context and implemented using a Kalman filter, and whereinthe denoising algorithm uses CGM data only intended for real-timeapplication.

Methods have been suggested to improve the accuracy of CGM readings byreducing random noise and calibration errors [13-17]. Accordingly, anaspect of an embodiment of the present invention method, system, andcomputer readable medium provides, but not limited thereto, usingreal-time short-time prediction (i.e. prediction with horizon less than20 minutes) to improve the accuracy of CGM devices by compensating partof the delay introduced by the low-pass nature of the BG-to-IG kineticsystem.

Continuous glucose monitoring (CGM) sensors assess blood glucose (BG)fluctuations indirectly—by measuring interstitial glucose (IG)concentration. However, IG and BG concentration time-series aredifferent because of the existence of a BG-to-G kinetics. The presenceof the BG-to-IG dynamics affects the accuracy of CGM devices, inparticular in the hypoglycemic range. For instance, the effect of theBG-to-IG dynamics is evident in the representative real dataset shown inFIG. 1, where BG samples (stars), obtained by a gold standardmeasurement technique by drawing a sample every 15 min, are comparedwith CGM measured in parallel through a commercial sensor device (line).CGM data appears to be delayed and slightly attenuated in amplitude withrespect to BG.

An aspect of an embodiment of the present invention method, system, andcomputer readable medium provides, but not limited thereto, the use ofreal-time short-time glucose prediction (i.e. with prediction horizon PHof less than 20 minutes) as a solution to improve the accuracy of CGMdevices. The core of the invention lies in substituting the current CGMvalue given in output by the sensors at time t, named CGM(t), with theglucose concentration predicted by a suitable algorithm PH minutes aheadin time, i.e. CGMNEW(t)=COM(t+PHIt). In particular, the implementationof short-time prediction here illustrated presents the followingimportant features: works in real-time, and can be either embedded incommercial CGM sensor or located in cascade. The proposed algorithm (andrelated method, technique, system, and computer readable medium) isrobust and has many advantages (as discussed later in the text), becausebut not limited thereto, it is developed in a stochastic context andimplemented using a Kalman filter. The procedure uses CGM data only andis intended for real-time application.

In order to demonstrate the effectiveness of the invention, it wastested by the present inventors retrospectively on 25 data setsconsisting of Freestyle Navigator™ traces (1-min sampling) and referenceBG time-series (15-min sampling) observed in parallel for up to 48 hrs.The accuracy of using the predicted CGM in place of the actual CGMoutput is assessed by the continuous glucose-error grid analysis(CG-EGA). Results demonstrate that a significant improvement in accuracyis achieved by using _(CGMNEW(t)) in place of CGM(t).

The root mean square error is reduced by 19% when an ad-hoc PH is tunedto each subject and by 14% when an “average” fixed PH is used for theentire population of patients. Finally, there is a significantimprovement at hypoglycaemia: the number of data points falling inaccurate or benign zones (A+B) of the CG-EGA increased by more than 20%.

Various objects and/or advantages of some preferred embodiments of theinvention can be, in some preferred examples, achieved via the featuresof the independent claims attached hereto. Additional preferredembodiments are further set forth in the dependent claims.

BRIEF DESCRIPTION OF DRAWINGS

The invention can be best understood from the following detaileddescription of exemplary embodiments of the invention taken inconjunction with the accompanying drawings.

FIG. 1 shows a time graph of representative subject. BG references(stars) vs. CGM data (line) profiles.

FIG. 2 shows a time graph of BG references (stars), CGM (line) andCGMNEw data (line) profiles.

FIG. 3 shows boxplots of RMSE between BG references and CGM data. Left:original CGM. Middle: CGMNEW with PH=12 minutes. Right: CGMNEW withindividualized PH. The line is the median value, the extremes of the boxare the 25^(th) and 75^(th) percentiles, and the whiskers extend to themost extreme data points.

FIG. 4 shows P-EGA (left) and R-EGA (right) comparing results obtainedfrom original CGM data (circles) with CGMNEW with PH=12 minutes (top,circles) and CGMNEW with individualized PH (bottom, circles).

FIG. 5 shows a block diagram illustrating an example of a machine uponwhich one or more aspects of embodiments of the present invention can beimplemented.

FIG. 6 shows a high level functional block diagram of an embodiment ofthe invention.

FIG. 7A shows a block diagram of a most basic configuration of acomputing device.

FIG. 7B shows a network system in which embodiments of the invention canbe implemented.

FIG. 8 shows a system in which one or more embodiments of the inventioncan be implemented using a network, or portions of a network orcomputers.

FIG. 9 shows a table of combined P-EGA and R-EGA results for originalCGM.

FIG. 10 shows a table of combined P-EGA and R-EGA results for originalCGMNEW with PH=12.

FIG. 11 shows a table of combined P-EGA and R-EGA results for _(CGMNEW)with individualized PH.

DETAILED DESCRIPTION

This invention provides a method, system, and device for improvingaccuracy of a continuous glucose monitoring through short-timeprediction. For example, the accuracy of a continuous glucose sensor isimproved.

In view of the many possible variations within the spirit of theinvention, the invention will be discussed with reference to exemplaryembodiments. However, it will be appreciated by those skilled in the artthat the following discussion is for demonstration purposes, and shouldnot be interpreted as a limitation of the invention. Other variationswithout departing from the spirit of the invention are applicable.

Mathematical Model Short-Time Prediction Algorithm

In order to perform short-time prediction, an online denoising methodrecently presented [14] was further developed and implemented by using aKalman filter (KF) coupled with a Bayesian smoothing criterion for theestimation of its unknown parameters.

In a stochastic context, let y(t) be the CGM value measured at time t:y(t)=u(t)+v(t)  (1)where u(t) is the true, unknown, glucose level and v(t) is random noise.The component v(t) is assumed to be additive, Gaussian, with zero meanand unknown variance equal to a². It has been proven that a suitable andefficient model to represent u(t) is the double integration of whitenoiseu(t)=2u(t−1)−u(t−2)+w(t)  (2)where w(t) is a zero mean Gaussian noise with (unknown) variance equalto A₂ [14]. The estimation of u(t) can be efficiently performed by usingKF [22]. Converting Equations (1) and (2) into state-space form, andconsidering as state vector x=[xi(t)x2(01, where xi(t)=u(t) andx₂(t)=u(t−1) we obtain

$\begin{matrix}{{{\begin{matrix}{{{x_{1}\left( {t + 1} \right)}\mspace{20mu} 2} - {1\mspace{14mu}\ldots\mspace{14mu}{x_{1}(t)}}} \\{{x_{2}\left( {t + 1} \right)} - {10{x_{2}(t)}}}\end{matrix} +}}\underset{0}{w(t)}} & \left( {3a} \right) \\{\;^{r}{y(t)} = {{1.1\mspace{20mu}{OIL}\mspace{20mu}\begin{matrix}{x_{1}(t)} \\{x_{2}\left( {0\_} \right.}\end{matrix}} + {v(t)}}} & \left( {3b} \right)\end{matrix}$where Equations (3a) and (3b) are the process update and the measurementequations that are used by KF to estimate X(t 1 t), which is linearminimum-variance estimate of the state vector obtainable from themeasurements y(t) collected until time t For equations and details onthe KF implementation we refer to [14,21-23].

The only unknown parameters are the variance of the process andmeasurement noise, i.e. A² and a² values. However, A² and a² valuescould be efficiently estimated using the Bayesian smoothing criterion of[14]. Notably, in this way KF parameters reflect the specificsignal-to-noise ratio (SNR) of the time series. This allows dealing withits variability between sensors and individuals and is a key advantagein denoising, which can be useful in prediction as well.

For our purposes, we consider the prediction step of the KFi(t+1t)=Fi(t t)  (4)where F is the state-transition matrix (see Equation 3a), and i(t+11t)is the state estimate based only on measurements collected until time t.Considering a suitable short-time prediction horizon (PH), one canre-iterate Equation (4) PH times, obtaining)¹¹((t+PH t)=FPH)¹¹((t t)  (5)The left side, i.e. ki(t+PHI t), is the predicted CGM value at time t+PHbased on CGM data available until time t. If the chosen PH is close tothe diffusion constant T of the BG-to-IG kinetics (typically estimatedat about 12 minutes [24] for the specific device used here), one mayspeculate that ki(t+PHI t) can approximate the BG level at time t. Theidea is thus to use short-time prediction and to substitute in real timethe CGM output at time t, i.e. CGM(t), by a new value, which isCGm NE _(w)(t)=i i(t+t)  (6)Note: It is assumed that the CGM output is given every 1 minute. If thesampling period Ts (in minutes) were different, the PH exponent inEquation (5) would become equal to PH divided by Ts, with the obviousconstraint of having PH equal to kTs, where k is an integer.

Experimental Data

The dataset used to demonstrate the effectiveness of the proposedalgorithm consists of 25 CGM traces for type I diabetic subjects, asubset of the database was previously reported in [25]. The CGM traceshave been obtained using the FreeStyle Navigator® (Abbott Diabetes Care,Alameda, Calif.), which operates with a sampling period of 1 minute. Inaddition, frequently measured BG references have been collected every 15minutes with YSI BG analyzer (YSI, Inc., Yellow Springs, Ohio) for aperiod of at least 24 hours (see FIG. 1 for a representative data set).

As in [14], the estimation of A² and a² values has been performed in thefirst 6-hour portion of CGM data using the same stochastically-basedsmoothing criterion. However, because in this way prediction cannot beperformed until 6 hours of data have been collected, it was decided touse an average value for the regularization parameter y=6²/A² (forinstance, set to 0.001) to perform prediction in the 0-6 hours timewindow. The short-time prediction algorithm has been applied to eachdataset simulating real-time working conditions. FIG. 2 shows theresults of the application on the algorithm to the same representativesubject of FIG. 1 (a section of the time window 20-36 hours isenlarged). The line is the original CGM profile, the stars are the BGreferences, and the line is the CGMNEW time series, obtained by applyingto the short-time prediction algorithm with PH of 12 minutes (this valueis taken from [24] and is the average estimated delay for the FreeStyleNavigator®). As evident by inspecting the graph, the output of theprediction algorithm _((CGMNEW)) appears able to compensate for part ofthe difference introduced by the BG-to-IG kinetics. The _(CGMNEW)profile overlaps the YSI measurements better than the original,improving the accuracy of the sensor, especially close to peaks (e.g.around hour 32) and nadirs (e.g. around hour 34). Quantitatively, theroot mean square error (RMSE) calculated between BG references and CGMdata decreases from 27 mg/dL to 20 mg/dL, an improvement of about 27%.

Quantitative results of the application of the algorithm to all 25datasets are graphically illustrated and summarized in the boxplots ofFIG. 3. The boxplot on the left is a compact representation of RMSEvalues calculated between BG references and original CGM data, while themiddle boxplot presents RMSE values calculated between BG and _(CGMNEW)data (PH=12). For each boxplot, the line is the median RMSE value, theextremes of the box are the 25^(th) and 75^(th) percentiles, and thewhiskers extend to the most extreme data points. Comparing the twoboxplots, the enhancement in the accuracy of CGM output is evident: theRMSE decreases significantly for all subjects (p=0.05). In particular,the median RMSE decreases from 22.1 to 192 mg/dL (a relative improvementof about 14%).

The second index used to quantify the improvement introduced by usingshort-time prediction algorithm is the continuous glucose-error gridanalysis (CG-EGA) [25]. The CG-EGA is one of the methods widely used forassessing the clinical accuracy of CGM data, and for reporting accuracyin each of three relevant glycemic ranges, hypoglycemia, euglycemia, andhyperglycemia. FIG. 4 shows P-EGA (left) and R-EGA (right) obtained byusing the _(CGMNEW) data with PH=12 (top, circles) and orignal CGM data(top, circles). A total of 2630 data pairs are included. The graphicsimply that the number of data pairs in the dangerous (D) zonesignificantly decreases (from 36.7% to 23.3%), thus confirming theresults of FIG. 2. This is also supported by the numerical results shownin FIG. 9, which presents the CG-EGA matrix [25] results when usingoriginal CGM data. The percentage of clinically accurate readings andreadings resulting m benign errors (i.e. A.B zone) is 63.3% athypoglycemia, 99.4% at euglycemia, and 99.7% in hyperglycemia. FIG. 10shows the results obtained by CGMNEW with PH=12 minutes. Compared withvalues shown in FIG. 9, the percentage of data pairs falling in A,B zoneincreases significantly to 76.7% at hypoglycemia (relative improvementof about 21′)/0), while the values for euglycemia and hyperglycemiaremain unchanged.

Because of the variability from individual to individual of thediffusion constant T of BG-to-IG kinetics [12,26], one can argue thatthe choice of a fixed PH could not be optimal. For this reason, we alsoperformed a second analysis, individualizing the PH value to understandif the performance of the algorithm could be further improved. For eachsubject, PH values from 1 to 30 minutes have been tested, and the PHvalue which returned the lowest RMSE has been retrospectively selectedas optimal. Results of this retrospective analysis show that theindividualized PH value varies from subject to subject, from a minimumof 5 minutes to a maximum of 22 minutes, confirming the results obtainedin [12,26] The boxplot summarizing RMSE results of the application ofthe short-time prediction algorithm with individualized PH is on theright side of FIG. 3. Comparing left (original CGM) and right boxplots,the RMSE decreases significantly for all subjects (p=0.04), with themedian RMSE decreasing from 22.1 to 18.0 mg/dL (a relative improvementof about 19%). However, comparing results of middle _((CGMNEW) withPH=12) and right boxplots, no significant differences have beenachieved. This result is also confirmed by the CG-EGA, graphicallydepicted in the panels of the bottom row of FIG. 4. Note that, even ifthe number of data pairs falling in the D zone decreases from 37.7% to21.8%, this results is not substantially different from the one obtainedwith PH=12 (23.3%). Finally, analyzing the results shown in FIG. 11, thepercentage of data pairs falling in A+B zone is also very similar to theresults obtained using a fixed PH. This evidences that the choice ofindividualizing the PH value does not result in significant improvementin the performance of the prediction algorithm.

FIG. 5 is a block diagram illustrating an example of a machine uponwhich one or more aspects of embodiments of the present invention can beimplemented.

FIG. 5 illustrates a block diagram of an example machine 400 upon whichone or more embodiments (e.g., discussed methodologies) can beimplemented (e.g., run).

Examples of machine 400 can include logic, one or more components,circuits (e.g., modules), or mechanisms. Circuits are tangible entitiesconfigured to perform certain operations. In an example, circuits can bearranged (e.g., internally or with respect to external entities such asother circuits) in a specified manner. In an example, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more hardware processors (processors) can be configured bysoftware (e.g., instructions, an application portion, or an application)as a circuit that operates to perform certain operations as describedherein. In an example, the software can reside (1) on a non-transitorymachine readable medium or (2) in a transmission signal. In an example,the software, when executed by the underlying hardware of the circuit,causes the circuit to perform the certain operations.

In an example, a circuit can be implemented mechanically orelectronically. For example, a circuit can comprise dedicated circuitryor logic that is specifically configured to perform one or moretechniques such as discussed above, such as including a special-purposeprocessor, a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC). In an example, a circuitcan comprise programmable logic (e.g., circuitry, as encompassed withina general-purpose processor or other programmable processor) that can betemporarily configured (e.g., by software) to perform the certainoperations. It will be appreciated that the decision to implement acircuit mechanically (e.g., in dedicated and permanently configuredcircuitry), or in temporarily configured circuitry (e.g., configured bysoftware) can be driven by cost and time considerations.

Accordingly, the term “circuit” is understood to encompass a tangibleentity, be that an entity that is physically constructed, permanentlyconfigured (e.g., hardwired), or temporarily (e.g., transitorily)configured (e.g., programmed) to operate in a specified manner or toperform specified operations. In an example, given a plurality oftemporarily configured circuits, each of the circuits need not beconfigured or instantiated at any one instance in time. For example,where the circuits comprise a general-purpose processor configured viasoftware, the general-purpose processor can be configured as respectivedifferent circuits at different times. Software can accordinglyconfigure a processor, for example, to constitute a particular circuitat one instance of time and to constitute a different circuit at adifferent instance of time.

In an example, circuits can provide information to, and receiveinformation from, other circuits. In this example, the circuits can beregarded as being communicatively coupled to one or more other circuits.Where multiple of such circuits exist contemporaneously, communicationscan be achieved through signal transmission (e.g., over appropriatecircuits and buses) that connect the circuits. In embodiments in whichmultiple circuits are configured or instantiated at different times,communications between such circuits can be achieved, for example,through the storage and retrieval of information in memory structures towhich the multiple circuits have access. For example, one circuit canperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further circuit canthen, at a later time, access the memory device to retrieve and processthe stored output. In an example, circuits can be configured to initiateor receive communications with input or output devices and can operateon a resource (e.g., a collection of information).

The various operations of method examples described herein can beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors can constitute processor-implementedcircuits that operate to perform one or more operations or functions. Inan example, the circuits referred to herein can compriseprocessor-implemented circuits.

Similarly, the methods described herein can be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod can be performed by one or more processors orprocessor-implemented circuits. The performance of certain of theoperations can be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In an example, the processor or processors can be located in asingle location (e.g., within a home environment, an office environmentor as a server farm), while in other examples the processors can bedistributed across a number of locations.

The one or more processors can also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations can be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., Application Program Interfaces (APIs).)

Example embodiments (e.g., apparatus, systems, or methods) can beimplemented in digital electronic circuitry, in computer hardware, infirmware, in software, or in any combination thereof. Exampleembodiments can be implemented using a computer program product (e.g., acomputer program, tangibly embodied in an information carrier or in amachine readable medium, for execution by, or to control the operationof, data processing apparatus such as a programmable processor, acomputer, or multiple computers).

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a software module(e.g. denoising module), subroutine, or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site or distributedacross multiple sites and interconnected by a communication network.

In an example, operations can be performed by one or more programmableprocessors executing a computer program to perform functions byoperating on input data and generating output. Examples of methodoperations can also be performed by, and example apparatus can beimplemented as, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)).

The computing system can include clients and servers. A client andserver are generally remote from each other and generally interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware can be a designchoice. Below are set out hardware (e.g., machine 400) and softwarearchitectures that can be deployed in example embodiments.

In an example, the machine 400 can operate as a standalone device or themachine 400 can be connected (e.g., networked) to other machines. In anetworked deployment, the machine 400 can operate in the capacity ofeither a server or a client machine in server-client networkenvironments. In an example, machine 400 can act as a peer machine inpeer-to-peer (or other distributed) network environments. The machine400 can be a personal computer (PC), a tablet PC, a set-top box (STB), aPersonal Digital Assistant (PDA), a mobile telephone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions (sequential or otherwise) specifying actions to be taken(e.g., performed) by the machine 400. Further, while only a singlemachine 400 is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

Example machine (e.g., computer system) 400 can include a processor 402(e.g., a central processing unit (CPU), a graphics processing unit (GPU)or both), a main memory 404 and a static memory 406, some or all ofwhich can communicate with each other via a bus 408. The machine 400 canfurther include a display unit 410, an alphanumeric input device 412(e.g., a keyboard), and a user interface (UI) navigation device 411(e.g., a mouse). In an example, the display unit 810, input device 417and UI navigation device 414 can be a touch screen display. The machine400 can additionally include a storage device (e.g., drive unit) 416, asignal generation device 418 (e.g., a speaker), a network interfacedevice 420, and one or more sensors 421, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor.

The storage device 416 can include a machine readable medium 422 onwhich is stored one or more sets of data structures or instructions 424(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 424 canalso reside, completely or at least partially, within the main memory404, within static memory 406, or within the processor 402 duringexecution thereof by the machine 400. In an example, one or anycombination of the processor 402, the main memory 404, the static memory406, or the storage device 416 can constitute machine readable media.While the machine readable medium 422 is illustrated as a single medium,the term “machine readable medium” can include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) that configured to store the one or moreinstructions 424. The term “machine readable medium” can also be takento include any tangible medium that is capable of storing, encoding, orcarrying instructions for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure or that is capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine readable medium” can accordingly be taken to include, but notbe limited to, solid-state memories, and optical and magnetic media.Specific examples of machine readable media can include non-volatilememory, including, by way of example, semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 424 can further be transmitted or received over acommunications network 426 using a transmission medium via the networkinterface device 420 utilizing any one of a number of transfer protocols(e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communicationnetworks can include a local area network (LAN), a wide area network(WAN), a packet data network (e.g., the Internet), mobile telephonenetworks (e.g., cellular networks), Plain Old Telephone (POTS) networks,and wireless data networks (e.g., IEEE 802.11 standards family known asWi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer(P2P) networks, among others. The term “transmission medium” shall betaken to include any intangible medium that is capable of storing,encoding or carrying instructions for execution by the machine, andincludes digital or analog communications signals or other intangiblemedium to facilitate communication of such software.

The machine 400 can be configured or arranged to include a denoisingmodule (e.g. software and/or hardware, circuit(s)) for generating animproved accuracy CGM from the signal received from the continuousglucose sensor (CGS) by reducing random noise and calibration errorsusing real-time short-time glucose prediction.

FIG. 6 is a high level functional block diagram of an embodiment of theinvention.

As shown in FIG. 6, a processor or controller 102 may communicate withthe glucose monitor or device 101, and optionally the insulin device100. The glucose monitor or device 101 may communicate with the subject103 to monitor glucose levels of the subject 103. The processor orcontroller 102 is configured to perform the required calculations.Optionally, the insulin device 100 may communicate with the subject 103to deliver insulin to the subject 103. The processor or controller 102is configured to perform the required calculations. The glucose monitor101 and the insulin device 100 may be implemented as a separate deviceor as a single device. The processor 102 can be implemented locally inthe glucose monitor 101, the insulin device 100, or a standalone device(or in any combination of two or more of the glucose monitor, insulindevice, or a stand along device). The processor 102 or a portion of thesystem can be located remotely such that the device is operated as atelemedicine device.

Referring to FIG. 7A, in its most basic configuration, computing device144 typically includes at least one processing unit 150 and memory 146.Depending on the exact configuration and type of computing device,memory 146 can be volatile (such as RAM), non-volatile (such as ROM,flash memory, etc.) or some combination of the two.

Additionally, device 144 may also have other features and/orfunctionality. For example, the device could also include additionalremovable and/or non-removable storage including, but not limited to,magnetic or optical disks or tape, as well as writable electricalstorage media. Such additional storage is provided by removable storage152 and non-removable storage 148. Computer storage media includesvolatile and nonvolatile, removable and non-removable media implementedin any method or technology for storage of information such as computerreadable instructions, data structures, program modules (e.g. denoisingmodule) or other data. The memory, the removable storage and thenon-removable storage are all examples of computer storage media.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology CDROM, digital versatiledisks (DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canaccessed by the device. Any such computer storage media may be part of,or used in conjunction with, the device. The device may also contain oneor more communications connections 154 that allow the device tocommunicate with other devices (e.g. other computing devices). Thecommunications connections carry information in a communication media.Communication media typically embodies computer readable instructions,data structures, program modules (e.g. denoising module) or other datain a modulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode, execute,or process information in the signal. By way of example, and notlimitation, communication medium includes wired media such as a wirednetwork or direct-wired connection, and wireless media such as radio,RF, infrared and other wireless media. As discussed above, the termcomputer readable media as used herein includes both storage media andcommunication media.

In addition to a stand-alone computing machine, embodiments of theinvention can also be implemented on a network system comprising aplurality of computing devices that are in communication with anetworking means, such as a network with an infrastructure or an ad hocnetwork. The network connection can be wired connections or wirelessconnections. As a way of example, FIG. 7B illustrates a network systemin which embodiments of the invention can be implemented. In thisexample, the network system comprises computer 156 (e.g. a networkserver), network connection means 158 (e.g. wired and/or wirelessconnections), computer terminal 160, and PDA (e.g. a smart-phone) 162(or other handheld or portable device, such as a cell phone, laptopcomputer, tablet computer, GPS receiver, mp3 player, handheld videoplayer, pocket projector, etc. or handheld devices (or non portabledevices) with combinations of such features). In an embodiment, itshould be appreciated that the module listed as 156 may be glucosemonitor device. In an embodiment, it should be appreciated that themodule listed as 156 may be a glucose monitor device and an insulindevice. Any of the components shown or discussed with FIG. 7B may bemultiple in number. The embodiments of the invention can be implementedin anyone of the devices of the system. For example, execution of theinstructions or other desired processing can be performed on the samecomputing device that is anyone of 156, 160, and 162. Alternatively, anembodiment of the invention can be performed on different computingdevices of the network system. For example, certain desired or requiredprocessing or execution can be performed on one of the computing devicesof the network (e.g. server 156 and/or glucose monitor device), whereasother processing and execution of the instruction can be performed atanother computing device (e.g. terminal 160) of the network system, orvice versa. In fact, certain processing or execution can be performed atone computing device (e.g. server 156 and/or glucose monitor device);and the other processing or execution of the instructions can beperformed at different computing devices that may or may not benetworked. For example, the certain processing can be performed atterminal 160, while the other processing or instructions are passed todevice 162 where the instructions are executed. This scenario may be ofparticular value especially when the PDA 162 device, for example,accesses to the network through computer terminal 160 (or an accesspoint in an ad hoc network). For another example, software to beprotected can be executed, encoded or processed with one or moreembodiments of the invention. The processed, encoded or executedsoftware can then be distributed to customers. The distribution can bein a form of storage media (e.g. disk) or electronic copy.

For example, a denoising module for generating an improved accuracy CGMsignal by reducing random noise and calibration errors using real-timeshort-time glucose prediction can be software and/or hardware configuredand/or arranged as described above.

FIG. 8 illustrates a system in which one or more embodiments of theinvention can be implemented using a network, or portions of a networkor computers.

FIG. 8 diagrammatically illustrates an exemplary system in whichexamples of the invention can be implemented. In an embodiment theglucose monitor may be implemented by the subject (or patient) at homeor other desired location. However, in an alternative embodiment it maybe implemented in a clinic setting or assistance setting. For instance,referring to FIG. 8, a clinic setup 158 provides a place for doctors(e.g. 164) or clinician/assistant to diagnose patients (e.g. 159) withdiseases related with glucose. A glucose monitoring device 10 can beused to monitor and/or test the glucose levels of the patient. It shouldbe appreciated that while only glucose monitor device 10 is shown in thesystem of the invention and any component thereof may be used in themanner depicted by FIG. 8. The system or component may be affixed to thepatient or in communication with the patient as desired or required. Forexample the system or combination of components thereof—including aglucose monitor device 10, a controller 12, or an insulin pump 14, orany other device or component—may be in contact or affixed to thepatient through tape or tubing or may be in communication through wiredor wireless connections. Such monitor and/or test can be short term(e.g. clinical visit) or long term (e.g. clinical stay or family). Theglucose monitoring device outputs can be used by the doctor (clinicianor assistant) for appropriate actions, such as insulin injection or foodfeeding for the patient, or other appropriate actions. Alternatively,the glucose monitoring device output can be delivered to computerterminal 168 for instant or future analyses. The delivery can be throughcable or wireless or any other suitable medium. The glucose monitoringdevice output from the patient can also be delivered to a portabledevice, such as PDA 166. The glucose monitoring device outputs withimproved accuracy can be delivered to a glucose monitoring center 172for processing and/or analyzing. Such delivery can be accomplished inmany ways, such as network connection 170, which can be wired orwireless. In addition to the glucose monitoring device outputs, errors,parameters for accuracy improvements, and any accuracy relatedinformation can be delivered, such as to computer 168, and/or glucosemonitoring center 172 for performing error analyses. This can provide acentralized accuracy monitoring and/or accuracy enhancement for glucosecenters, due to the importance of the glucose sensors.

Examples of the invention can also be implemented in a standalonecomputing device associated with the target glucose monitoring device.An exemplary computing device in which examples of the invention can beimplemented is schematically illustrated in FIG. 7A.

In summary, having accurate readings from a continuous glucosemonitoring (CGM) device is essential to make CGM systems even morereliable in a daily-life application perspective, in particular becausethe more the accuracy of CGM device the better the real-time detectionof hypoglycemic and hyperglycemic events. Nowadays, the accuracy of CGMdevices is still suboptimal because problems related to calibrationerrors and the presence of the BG-to-IG kinetic system which alsoaffects the calibration process.

The short-time prediction, i.e. with prediction horizon PH less than 20minutes, should be considered as an effective solution to improve theaccuracy of CGM devices since it compensates part of the delay due tothe BG-to-IG kinetic system.

This aspect can be potentially of commercial interest for CGMmanufacturers because the suboptimal accuracy of CGM sensors is one ofthe factors that do not allow CGM to be accepted by FDA as substitute ofself monitoring finger-sticks.

Another important feature obtained as a by-product by an aspect of anembodiment of the present invention is the real-time prediction of thefuture glucose concentration. The possibility of generating also apreventive alert before the event occurs can have a potential impact forCGM manufacturers, because it can make any alert generation systemtimelier in alarming the patient for hypo/hyperglycemic events.

U.S. Pat. No. 7,806,886 by Medtronic provides a filter that presentsseveral limitations. First, it needs to be identified on specific data,second its structure needs to be modified if applied to CGM devicesother than the ones of Medtronic (because the sampling period maychange), third, because the “raw deconvolution” is exposed toill-conditioning (see De Nicolao et al. [27]).

The PCT publication No. 2007027691/WO-AI (PCT Application No.PCT/US20061033724), entitled “Improving the accuracy of continuousglucose sensors” provides the BG reconstruction that is performed by anumerical approximation, which may in limited instances be exposed toill-conditioning of inverse problems.

An aspect of an embodiment of the present invention method, system, andcomputer readable medium provides, but not limited thereto, aninnovation that lies in using a short-time prediction to improveaccuracy of CGM readings by compensating part of the delay with BGmeasurements due to the BG-to-IG kinetics. The implementation ofshort-time prediction here proposed is optimal with respect to otherimplementations, because is able to take into account possible SNRvariations of CGM data during the monitoring, thanks to an automaticstochastically-based Bayesian estimation procedure of the unknownparameters of the algorithm.

Both methods of the U.S. Pat. No. 7,806,886 patent and the2007027691/VVO-AI application are very different from the solutionproposed in the present invention, because but not limited thereto, noneof those methods exploits short-time prediction to compensate the delaydue to the presence of the BG-to-IG kinetics.

An aspect of various embodiments of the present invention may provide anumber of advantages, such as but not limited thereto, a short-timeprediction that is an effective solution to improve CGM accuracy andcompensating part of the delay with BG measurements due to the BG-to-IGkinetics. First, it does not contain any physiological model to beidentified. Second, it does not need to be modified if the CGM devicechanges. Third, the proposed implementation circumvents ill-conditioningof inverse problems.

An aspect of various embodiments of the present invention may beutilized for a number of products and services, such as but not limitedthereto, a commercial impact on CGM devices. In fact, in the preliminarystudy that the present inventors performed, we showed that it allowsimproving the accuracy of the output of CGM sensors by more than 15%. Assaid above, this can be of interest for CGM manufacturers because thesuboptimal accuracy of CGM sensors is one of the factors that do notallow CGM to be accepted as substitute of finger-stick measures. Inaddition, the more accurate CGM readings, the better the hypoglycemiaand hyperglycemia detection.

Finally, the improvement of the accuracy of CGM data of variousembodiments of the present invention can be important also for real-timeapplications based on CGM data, e.g. for the improvement of the accuracyof the CGM signal, which is a key element in closed-loop algorithms forartificial pancreas experiments.

APPENDIX A

The following patents, applications and publications as listed below andthroughout this document are hereby incorporated by reference in theirentirety herein (and which are not admitted to be prior art with respectto the present invention by inclusion in this section).

-   1 Garg K, Zisser H, Schwartz S, Bailey T, Kaplan R, Ellis S,    Jovanovic L. Improvement in glycernic excursions with a    transcutaneous, real-time continuous glucose sensor. Diabetes Care.    2006:29(1):44-50.-   2. Klonoff D C. Continuous glucose monitoring: Roadmap for 21st    century diabetes therapy. Diabetes Care. 2005; 28(5):1231-9.    -   Deiss D, Bolinder J, Riveline J, Battelino T, Bosi E,        Tubiana-Ruff N, Kerr D, Phillip M. Improved glycemic control in        poorly controlled patients with type 1 diabetes using real-time        continuous glucose monitoring. Diabetes Care. 2006;        29(12):2730-2,-   4. Juvenile Diabetes Research Foundation Continuous Glucose    Monitoring Study Group, Tamborlane V W/, Beck R W, Bode B W,    Buckingham B, Chase H P, Clemons R, Fiallo-Scharer R, Fox L A,    Gilliam L K, Hirsch 1B, Huang E S, Kollman C, Kowalski A J, Laffel    L, Lawrence J M, Lee J, Mauras N, O'Grady M, Ruedy K J, Tansey M,    Tsalikian E, Weinzimer S, Wilson D M, Wolpert H, Wysocki T, Xing D.    Continuous glucose monitoring and intensive treatment of type 1    diabetes, N Engl J Med, 2008; 359(14): 1464-76.-   5. Bequette B W. A critical assessment of algorithms and challenges    in the development of a closed-loop artificial pancreas. Diabetes    Technol Ther. 2005; 7(1):28-47.-   6. Cobelli C, Dalla Man C, Sparacino G, Magni L, De Nicolao G,    Kovatchev B P. Diabetes: Models, Signals, and Control. IEEE rev    biomed Eng. 2010; 2(2):54-96.-   7. Bruttomesso D, Farret A, Costa S, Marescotti M C, Vettore M,    Avogaro A, Tiengo A, Dalla Man C, Place J, Facchinetti A, Guerra S,    Magni L, De Nicolao G, Cobelli C, Renard E, Maran A. Closed-Loop    Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin    Delivery and a Model Predictive Control Algorithm: Preliminary    Studies in Padova and Montpellier. J Diabetes Sci Technol. 2009;    3(5): 1014-21.-   8. www.diadvisor.eu [accessed on Mar. 29, 2010].-   9. Kovatchev B, Anderson S, Heinemann L, Clarke W. Comparison of the    numerical and clinical accuracy of four continuous glucose monitors.    Diabetes Care. 2008; 31(6):1160-4.-   10. Clarke W L, Anderson S, Kovatchev B. Evaluating clinical    accuracy of continuous glucose monitoring systems: Continuous    Glucose-Error Grid Analysis (CG-EGA). Curr Diabetes Rev. 2008;    4(3):193-9. Review.-   11. Rebrin K, Steil G M, van Antwerp W P, Subcutaneous glucose    predicts plasma glucose independent of insulin: implications for    continuous monitoring. Am J Physiol. 1999; 277(3 Pt 1):E561-71.-   12. Keenan D B, Mastrototaro J J, Voskanyan G, Steil G M. Delays in    Minimally Invasive Continuous Glucose Monitoring Devices: A Review    of Current Technology. J Diabetes Sci Technol. 2009; 3(5):1207-14.-   13. Facchinetti A, Sparacino G, Cobelli C. Modeling the Error of    Continuous Glucose Monitoring Sensor Data: Critical Aspects    Discussed through Simulation Studies. J Diabetes Sci Technol. 2010;    4(1):4-14.-   14. Facchinetti A, Sparacino G, Cobelli C. An online self-tunable    method to denoise CGM sensor data. IEEE Trans Biomed Eng. 2010;    57(3):634-41.-   15. Diabetes Research In Children Network (Direcnet) Study Group,    Buckingham B A, Kollman C, Beck R, Kalajian A, Fiallo-Scharer R,    Tansey M J, Fox L A, Wilson D M, Weinzimer S A, Ruedy K J,    Tamborlane W V Evaluation of factors affecting CGMS calibration.    Diabetes Technol Ther. 2006; 8(3):318-25.-   16. King C, Anderson S M, Breton M, Clarke W L, Kovatchev B P.    Modeling of calibration effectiveness and blood-to-interstitial    glucose dynamics as potential confounders of the accuracy of    continuous glucose sensors during hyperinsulinemic clamp. J Diabetes    Sci Technol. 2007; 1(3):317-22.-   17. Kuure-Kinsey M, Palerm C C, Bequette B W. A dual-rate Kalman    filter for continuous glucose monitoring. Conf Proc IEEE Eng Med    Biol Soc. 2006; 1:63-6.-   18. Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti A,    Cobelli C. Glucose concentration can be predicted ahead in time from    continuous glucose monitoring sensor time-series. IEEE Trans Biomed    Eng. 2007; 54(5):931-7.-   19. Gani A, Gribok A V, Rajaraman S, Ward W K, Reifman J. Predicting    subcutaneous glucose concentration in humans: data-driven glucose    modeling. IEEE Trans Biomed Eng. 2009; 56(2):246-54.-   20. Perez-Gandia C, Facchinetti A, Sparacino G, Cobelli C, GOmez E    J, Rigla M, de Leiva A, Hernando M E. Artificial neural network    algorithm for online glucose prediction from continuous glucose    monitoring. Diabetes Technol Ther. 2010; 12(1):81-8.-   21. Palerm C C, Willis J P, Desemone J, Bequette B W. Hypoglycemia    prediction and detection using optimal estimation. Diabetes Technol    Ther. 2005; 7(1):3-14.-   22. Anderson B D O and Moore J B. Optimal Filtering. Dover    Publications, 2005.-   23. Bequette B W. Continuous glucose monitoring: real-time    algorithms for calibration, filtering, and alarms. J Diabetes Sci    Technol. 2010; 4(2):404-18.-   24. Keenan D B, Mastrototaro J J, Voskanyan, Steil G. Delays in    Minimally Invasive Continuous Glucose Monitoring Devices: A Review    of Current Technology. J Diabetes Sci Technol. 2009; 3(5): 1207-14.-   25. Kovatchev B P, Gonder-Frederick L A, Cox D J, Clarke W L.    Evaluating the accuracy of continuous glucose-monitoring sensors:    continuous glucose-error grid analysis illustrated by TheraSense    Freestyle Navigator data. Diabetes Care. 2004; 27(8):1922-8.-   26. Facchinetti A, Sparacino G, Cobelli C. Reconstruction of glucose    in plasma from interstitial fluid continuous glucose monitoring    data: role of sensor calibration. J Diabetes Sci Technol. 2007; 1    (5):617-23.-   27. De Nicolao G, Sparacino G, Cobelli C. Nonparametric input    estimation in physiological systems: problems, methods and case    study. Automatica. 1997; 33:851-70.

APPENDIX B

The devices, systems, non-transitory computer readable medium, andmethods of various embodiments of the invention disclosed herein mayutilize aspects disclosed in the following references, applications,publications and patents and which are hereby incorporated by referenceherein in their entirety (and which are not admitted to be prior artwith respect to the present invention by inclusion in this section):

-   A. International Patent Application No. PCT/US2014/045393 entitled    -   “Simulation of Endogenous and Exogenous Glucose/Insulin/Glucagon        interplay in Type 1 Diabetic Patients,” filed Jul. 3, 2014.-   B. U.S. patent application Ser. No. 14/266,612 entitled “Method,    System and Computer Program Product for Real-Time Detection of    Sensitivity Decline in Analyte Sensors,” filed Apr. 30, 2014.-   C. U.S. patent application Ser. No. 13/418,305 entitled “Method,    System and Computer Program Product for Real-Time Detection of    Sensitivity Decline in Analyte Sensors,” filed Mar. 12, 2012; U.S.    Pat. No. 8,718,958, issued May 6, 2014.-   D. International Patent Application No. PCT/US2007/082744 entitled    “Method, System and Computer Program Product for Real-Time Detection    of Sensitivity Decline in Analyte Sensors,” filed Oct. 26, 2007.-   E. U.S. patent application Ser. No. 11/925,689 entitled “Method,    System and Computer Program Product for Real-Time Detection of    Sensitivity Decline in Analyte Sensors,” filed Oct. 26, 2007; U.S.    Pat. No. 8,135,548, issued Mar. 13, 2012.-   F. U.S. patent application Ser. No. 14/241,383 entitled “Method,    System and Computer Readable Medium for Adaptive Advisory Control of    Diabetes,” filed Feb. 26, 2014.-   G. International Patent Application No. PCT/US2012/052422 entitled    “Method, System and Computer Readable Medium for Adaptive Advisory    Control of Diabetes,” filed Aug. 26, 2012.-   H International Patent Application No. PCT/US2014/017754 entitled    “Method and System for Model-Based Tracking of Changes in Average    Glycemia in Diabetes,” filed Feb. 21, 2014.-   I. U.S. patent application Ser. No. 14/128,922 entitled “Unified    Platform for Monitoring and Control of Blood Glucose Levels in    Diabetic Patients,” filed Dec. 23, 2013.-   J. International Patent Application No. PCT/US2012/043910 entitled    “Unified Platform for Monitoring and Control of Blood Glucose Levels    in Diabetic Patients,” filed Jun. 23, 2012.-   K. U.S. patent application Ser. No. 14/128,811 entitled “Methods and    Apparatus for Modular Power Management and Protection of Critical    Services in Ambulatory Medical Devices,” filed Dec. 23, 2013.-   L. International Patent Application No. PCT/US2012/043883 entitled    “Methods and Apparatus for Modular Power Management and Protection    of Critical Services in Ambulatory Medical Devices,” filed Jun. 22,    2012.-   M. U.S. patent application Ser. No. 29/467,039 entitled “Alarm Clock    Display of Personal Blood Glucose Level,” filed Sep. 13, 2013.-   N U.S. patent application Ser. No. 14/015,831 entitled “CGM-Based    Prevention of Hypoglycemia via Hypoglycemia Risk Assessment and    Smooth Reduction Insulin Delivery,” filed Aug. 30, 2013.-   O. U.S. patent application Ser. No. 13/203,469 entitled “CGM-Based    Prevention of Hypoglycemia via Hypoglycemia Risk Assessment and    Smooth Reduction Insulin Delivery,” filed Aug. 25, 2011; U.S. Pat.    No. 8,562,587, issued Oct. 22, 2013.-   P. International Patent Application No. PCT/US2010/025405 entitled    “CGM-Based Prevention of Hypoglycemia via Hypoglycemia Risk    Assessment and Smooth Reduction Insulin Delivery,” filed Feb. 25,    2010.-   Q. International Patent Application No. PCT/US2013/053664 entitled    “Method, System, and Computer Simulation for Testing and Monitoring    of Treatment Strategies for Stress Hyperglycemia in the ICU,” filed    Aug. 5, 2013.-   R. U.S. patent application Ser. No. 13/637,359 entitled “Method,    System, and Computer Program Product for Improving the Accuracy of    Glucose Sensors Using Insulin Delivery Observation in Diabetes,”    filed Sep. 25, 2012; U.S. Patent Application Publication No.    2013/0079613, Mar. 28, 2013.-   S. International Patent Application No. PCT/US2011/029793 entitled    “Method, System, and Computer Program Product for Improving the    Accuracy of Glucose Sensors Using Insulin Delivery Observation in    Diabetes,” filed Mar. 24, 2011.-   T U.S. patent application Ser. No. 13/634,040 entitled “Method and    System for the Safety, Analysis, and Supervision of Insulin Pump    Action and Other Modes of Insulin Delivery in Diabetes,” filed Sep.    11, 2012; U.S. Patent Application Publication No. 2013/0116649, May    9, 2013.-   U. International Patent Application No. PCT/US2011/028163 entitled    “Method and System for the Safety, Analysis, and Supervision of    Insulin Pump Action and Other Modes of Insulin Delivery in    Diabetes,” filed Mar. 11, 2011.-   V. U.S. patent application Ser. No. 13/394,091 entitled “Tracking    the Probability for Imminent Hypoglycemia in Diabetes from    Self-Monitoring Blood Glucose (SMBG) Data,” filed Mar. 2, 2012; U.S.    Patent Application Publication No. 2012/0191361, Jul. 26, 2012.-   W. International Patent Application No. PCT/US2010/047711 entitled    “Tracking the Probability for Imminent Hypoglycemia in Diabetes from    Self-Monitoring Blood Glucose (SMBG) Data,” filed Sep. 2, 2010.-   X. U.S. patent application Ser. No. 13/393,647 entitled “System,    Method and Computer Program Product for Adjustment of Insulin    Delivery (AID) in Diabetes Using Nominal Open-Loop Profiles,” filed    Mar. 1, 2012; U.S. Patent Application Publication No. 2012/0245556,    Sep. 27, 2012.-   Y International Patent Application No. PCT/US2010/047386 entitled    “System, Method and Computer Program Product for Adjustment of    Insulin Delivery (AID) in Diabetes Using Nominal Open-Loop    Profiles,” filed Aug. 31, 2010.-   Z. U.S. patent application Ser. No. 13/380,839 entitled “System,    Method, and Computer Simulation Environment for in Silico Trials in    Pre-Diabetes and Type 2 Diabetes,” filed Dec. 25, 2011; U.S.

Patent Application Publication No. 2012/0130698, May 24, 2012.

-   AA. International Patent Application No. PCT/US2010/040097 entitled    “System, Method, and Computer Simulation Environment for in Silico    Trials in Prediabetes and Type 2 Diabetes,” filed Jun. 25, 2010.-   BB. U.S. patent application Ser. No. 13/322,943 entitled “System    Coordinator and Modular Architecture for Open-Loop and Closed-Loop    Control of Diabetes,” filed Nov. 29, 2011; U.S. Patent Application    Publication No. 2012/0078067, Mar. 29, 2012.-   CC. International Patent Application No. PCT/US2010/036629 entitled    “System Coordinator and Modular Architecture for Open-Loop and    Closed-Loop Control of Diabetes,” filed May 28, 2010.-   DD. U.S. patent application Ser. No. 13/131,467 entitled “Method,    System, and Computer Program Product for Tracking of Blood Glucose    Variability in Diabetes,” filed May 26, 2011; U.S. Patent    Application Publication No. 2011/0264378, Oct. 27, 2011.-   EE. International Patent Application No. PCT/US2009/065725 entitled    “Method, System, and Computer Program Product for Tracking of Blood    Glucose Variability in Diabetes,” filed Nov. 24, 2009.-   FF. U.S. patent application Ser. No. 12/975,580 entitled “Method,    System, and Computer Program Product for the Evaluation of Glycemic    Control in Diabetes from Self-Monitoring Data,” filed Dec. 22, 2010.-   GG. U.S. patent application Ser. No. 11/305,946 entitled “Method,    System, and Computer Program Product for the Evaluation of Glycemic    Control in Diabetes from Self-Monitoring Data,” filed Dec. 19, 2005;    U.S. Pat. No. 7,874,985, issued Jan. 25, 2011.-   HH. U.S. patent application Ser. No. 10/240,228 entitled “Method,    System, and Computer Program Product for the Evaluation of Glycemic    Control in Diabetes from Self-Monitoring Data,” filed Sep. 26, 2002;    U.S. Pat. No. 7,025,425, issued Apr. 11, 2006.-   II. International Patent Application No. PCT/US2001/009884 entitled    “Method, System, and Computer Program Product for the Evaluation of    Glycemic Control in Diabetes,” filed Mar. 29, 2001.-   JJ. U.S. patent application Ser. No. 12/674,348 entitled “Method,    Computer Program Product and System for individual Assessment of    Alcohol Sensitivity,” filed Feb. 19, 2010.-   KK. International Patent Application No. PCT/US2008/073738 entitled    “Method, Computer Program Product and System for individual    Assessment of Alcohol Sensitivity,” filed Aug. 20, 2008.-   LL. U.S. patent application Ser. No. 12/665,149 entitled “Method,    System and Computer Program Product for Evaluation of Insulin    Sensitivity, Insulin/Carbohydrate Ratio, and Insulin Correction    Factors in Diabetes from Self-Monitoring Data,” filed Dec. 17, 2009.-   MM. International Patent Application No. PCTIUS2008/069416 entitled    “Method, System and Computer Program Product for Evaluation of    Insulin Sensitivity, Insulin/Carbohydrate Ratio, and Insulin    Correction Factors in Diabetes from Self-Monitoring Data,” filed    Jul. 8, 2008.-   NN. U.S. patent application Ser. No. 12/664,444 entitled “Method,    System and Computer Simulation Environment for Testing of Monitoring    and Control Strategies in Diabetes,” filed Dec. 14, 2009.-   OO. International Patent Application No. PCT/US2008/067725 entitled    “Method, System and Computer Simulation Environment for Testing of    Monitoring and Control Strategies in Diabetes,” filed Jun. 20, 2008.-   PP. U.S. patent application Ser. No. 12/516,044 entitled “Method,    System, and Computer Program Product for the Detection of Physical    Activity by Changes in Heart Rate, Assessment of Fast Changing    Metabolic States, and Applications of Closed and Open Control Loop    in Diabetes,” filed May 22, 2009; U.S. Pat. No. 8,585,593, issued    Nov. 19, 2013.-   QQ International Patent Application No. PCT/US2007/085588 entitled    “Method, System, and Computer Program Product for the Detection of    Physical Activity by Changes in Heart Rate, Assessment of Fast    Changing Metabolic States, and Applications of Closed and Open    Control Loop in Diabetes,” filed Nov. 27, 2007.-   RR. U.S. patent application Ser. No. 12/159,891 entitled “Method,    System and Computer Program Product for Evaluation of Blood Glucose    Variability in Diabetes from Self-Monitoring Data,” filed Jul. 2,    2008.-   SS. International Patent Application No. PCT/US2007/000370 entitled    “Method, System and Computer Program Product for Evaluation of Blood    Glucose Variability in Diabetes from Self-Monitoring Data,” filed    Jan. 5, 2007.-   TT. U.S. patent application Ser. No. 12/139,976 entitled “Method,    Apparatus and Computer Program Product for Assessment of Attentional    Impairments,” filed Jun. 16, 2008; U.S. Pat. No. 8,340,752, issued    Dec. 25, 2012.-   UU. U.S. patent application Ser. No. 10/476,826 entitled “Method,    Apparatus, and Computer Program Product for Assessment of    Attentional Impairments,” filed Nov. 3, 2003; U.S. Pat. No.    7,403,814, issued Jul. 22, 2008.-   W. International Patent Application No. US02/14188 entitled “Method,    Apparatus, and Computer Program Product for Assessment of    Attentional Impairments,” filed May 6, 2002.-   WW. U.S. patent application Ser. No. 12/065,257 entitled “Accuracy    of Continuous Glucose Sensors,” filed Feb. 28, 2008; U.S. Patent    Application Publication No. 2008/0314395, Dec. 25, 2008.-   XX. International Patent Application No. PCT/US2006/033724 entitled    “Method for Improvising Accuracy of Continuous Glucose Sensors and a    Continuous Glucose Sensor Using the Same,” filed Aug. 29, 2006.-   YY. U.S. patent application Ser. No. 11/943,226 entitled “Systems,    Methods and Computer Program Codes for Recognition of Patterns of    Hyperglycemia and Hypoglycemia, increased Glucose Variability, and    ineffective Self-Monitoring in Diabetes,” filed Nov. 20, 2007; U.S.    Patent Application Publication No. 2008/0154513, Jun. 26, 2008.-   ZZ. U.S. patent application Ser. No. 11/578,831 entitled “Method,    System and Computer Program Product for Evaluating the Accuracy of    Blood Glucose Monitoring Sensors/Devices,” filed Oct. 18, 2006; U.S.    Pat. No. 7,815,569, issued Oct. 19, 2010.-   AAA. International Patent Application No. US2005/013792 entitled    “Method, System and Computer Program Product for Evaluating the    Accuracy of Blood Glucose Monitoring Sensors/Devices,” filed Apr.    21, 2005.-   BBB. U.S. patent application Ser. No. 10/592,883 entitled “Method,    Apparatus, and Computer Program Product for Stochastic    Psycho-physiological Assessment of Attentional Impairments,” filed    Sep. 15, 2006; U.S. Pat. No. 7,761,144, issued Jul. 20, 2010.-   CCC. International Patent Application No. US2005/008908 entitled    “Method, Apparatus, and Computer Program Product for Stochastic    Psycho-physiological Assessment of Attentional Impairments,” filed    Mar. 17, 2005.-   DDD. U.S. patent application Ser. No. 10/524,094 entitled “Method,    System, and Computer Program Product for the Processing of    Self-Monitoring Blood Glucose (SMBG) Data To Enhance Diabetic    Self-Management,” filed Feb. 9, 2005; U.S. Pat. No. 8,538,703,    issued Sep. 17, 2013.-   EEE. International Patent Application No. PCT/US2003/025053 entitled    “Managing and Processing Self-Monitoring Blood Glucose,” filed Aug.    8, 2003.-   FFF. International Patent Application No. PCT/US2002/005676 entitled    “Method and Apparatus for the Early Diagnosis of Subacute,    Potentially Catastrophic Illness,” filed Feb. 27, 2002.-   GGG. U.S. patent application Ser. No. 09/793,653 entitled “Method    and Apparatus for the Early Diagnosis of Subacute, Potentially    Catastrophic Illness,” filed Feb. 27, 2001; U.S. Pat. No. 6,804,551    ISSUED Oct. 12, 2004.-   HHH. U.S. patent application Ser. No. 10/069,674 entitled “Method    and Apparatus for Predicting the Risk of Hypoglycemia,” filed Feb.    22, 2002; U.S. Pat. No. 6,923,763, issued Aug. 2, 2005.-   III. International Patent Application No. US00/22886 entitled    “Method and Apparatus for Predicting the Risk of Hypoglycemia,”    filed Aug. 21, 2000.

In summary, while the present invention has been described with respectto specific embodiments, many modifications, variations, alterations,substitutions, and equivalents will be apparent to those skilled in theart. The present invention is not to be limited in scope by the specificembodiment described herein. Indeed, various modifications of thepresent invention, in addition to those described herein, will beapparent to those of skill in the art from the foregoing description andaccompanying drawings. Accordingly, the invention is to be considered aslimited only by the spirit and scope of the disclosure, including allmodifications and equivalents.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthis application. For example, regardless of the content of any portion(e.g., title, field, background, summary, abstract, drawing figure,etc.) of this application, unless clearly specified to the contrary,there is no requirement for the inclusion in any claim herein or of anyapplication claiming priority hereto of any particular described orillustrated activity or element, any particular sequence of suchactivities, or any particular interrelationship of such elements.Moreover, any activity can be repeated, any activity can be performed bymultiple entities, and/or any element can be duplicated. Further, anyactivity or element can be excluded, the sequence of activities canvary, and/or the interrelationship of elements can vary. Unless clearlyspecified to the contrary, there is no requirement for any particulardescribed or illustrated activity or element, any particular sequence orsuch activities, any particular size, speed, material, dimension orfrequency, or any particularly interrelationship of such elements.Accordingly, the descriptions and drawings are to be regarded asillustrative in nature, and not as restrictive. Moreover, when anynumber or range is described herein, unless clearly stated otherwise,that number or range is approximate. When any range is described herein,unless clearly stated otherwise, that range includes all values thereinand all sub ranges therein. Any information in any material (e.g., aUnited States/foreign patent, United States/foreign patent application,book, article, etc.) that has been incorporated by reference herein, isonly incorporated by reference to the extent that no conflict existsbetween such information and the other statements and drawings set forthherein. In the event of such conflict, including a conflict that wouldrender invalid any claim herein or seeking priority hereto, then anysuch conflicting information in such incorporated by reference materialis specifically not incorporated by reference herein.

The invention claimed is:
 1. A method for improving the accuracy of acontinuous glucose monitoring system (CGM) comprising: obtaining anoutput CGM signal at a time t from a CGM device; removing random noisefrom said CGM signal by estimating a value of the CGM signal at a timet+PH using a noise state estimation, where PH is a preselected real-timeshort-time prediction horizon, the prediction horizon being a value thatmatches that of a diffusion constant of the bloodglucose-to-interstitial glucose (BG-to-IG) kinetics for the CGM device,wherein the estimated value of the CGM signal at time t+PH is a closerapproximation of blood glucose than the output CGM signal at time t;substituting in real time the estimated value of the CGM signal at timet+PH for the output CGM signal at time t; and using the estimated valueof the CGM signal at time t+PH as the true value of the CGM signal attime t.
 2. The method according to claim 1, further comprising using aprediction horizon PH of less than 20 minutes.
 3. The method accordingto claim 2, further comprising compensating part of a delay introducedby low-pass characteristic of a blood glucose-to-interstitial (BG-to-IG)kinetic system.
 4. The method according to claim 1, further comprisingcompensating part of a delay introduced by a low-pass characteristic ofa blood glucose-to-interstitial (BG-to-IG) kinetic system.
 5. The methodaccording to claim 1, further comprising substituting a current CGMvalue given in output by a CGM sensor at time t, named CGM(t), with theglucose concentration predicted by a noise-estimation algorithm PHminutes ahead in time.
 6. The method according to claim 5, furthercomprising developing the algorithm in a stochastic context andimplemented using a Kalman filter.
 7. The method according to claim 1,further comprising using CGM data only for real-time application.
 8. Amethod for improving the accuracy of a continuous glucose monitoring(CGM) sensor comprising: improving accuracy of CGM readings by reducingrandom noise and calibration errors in said readings using a real-timeshort-time prediction horizon, the prediction horizon being a value thatmatches that of a diffusion constant of the bloodglucose-to-interstitial glucose (BG-to-IG) kinetics for the CGM sensor;and denoising said CGM readings by using a Kalman filter (KF) coupledwith a Bayesian smoothing criterion for the estimation of its unknownparameters such that the denoised CGM readings are a closerapproximation of blood glucose than the un-denoised CGM readings.
 9. Asystem for improving the accuracy of a continuous glucose monitoringsensor comprising: a digital processor; a continuous glucose monitoring(CGM) sensor in communication with the digital processor, the continuousglucose monitoring (CGM) sensor configured to generate a glucose signal;and a denoising module, configured to receive the glucose signal fromthe continuous glucose monitoring (CGM) sensor, and generate an improvedaccuracy CGM signal by reducing random noise and calibration errorsusing a real-time short-time glucose prediction horizon (PH) to estimatethe real time denoised value of the CGM signal, the PH being a valuethat matches that of a diffusion constant of the bloodglucose-to-interstitial glucose (BG-to-IG) kinetics for the CGM sensor,the real time denoised value of the CGM signal being a closerapproximation of blood glucose than the glucose signal.
 10. The systemaccording to claim 9, wherein the denoising module is configured to usea prediction horizon PH of less than 20 minutes.
 11. The systemaccording to claim 9, wherein the denoising module is configured tocompensate part of a delay introduced by a low-pass characteristic of ablood glucose-to-interstitial (BG-to-IG) kinetic system.
 12. The systemaccording to claim 9 wherein the denoising module is configured tosubstitute a current CGM value given in output by a CGM sensor at timet, named CGM(t), with the glucose concentration predicted by a noiseestimation algorithm PH minutes ahead in time.
 13. The system accordingto claim 12, wherein the algorithm is developed in a stochastic contextand implemented using a Kalman filter.
 14. The system according to claim12, wherein the denoising algorithm uses CGM data only for real-timeapplication.
 15. The system according to claim 9, wherein the algorithmis developed in a stochastic context and implemented using a Kalmanfilter.
 16. The system according to claim 9, wherein the denoisingalgorithm uses CGM data only for real-time application.
 17. A method ofincreasing accuracy of a continuous glucose monitoring (CGM) sensorsignal measured at a time t, by using a short-time prediction horizon(PH) to estimate the value of the CGM signal at time t+PH by applyingCGM data obtained at time t to a Kalman filter for time t+PH, andsubstituting the [the] estimated value of CGM signal at time t+PH forthe real time CGM signal value obtained at time t, the PH being a valuethat matches that of a diffusion constant of the bloodglucose-to-interstitial glucose (BG-to-IG) kinetics for the CGM sensor,wherein the estimated value of the CGM signal at time t+PH is a closerapproximation of blood glucose than the CGM signal value obtained attime t.